Pattern inspection system

ABSTRACT

A pattern inspection system inspects an image of an inspection target pattern of an electronic device using an identifier constituted by machine learning, based on the image of the inspection target pattern of the electronic device and data used to manufacture the inspection target pattern. The system includes a storage unit which stores a plurality of pattern images of the electronic device and pattern data used to manufacture a pattern of the electronic device, and an image selection unit which selects a learning pattern image used in the machine learning from the plurality of pattern images, based on the pattern data and the pattern image stored in the storage unit.

CROSS REFERENCE TO RELATED APPLICATION

This application is a Continuation Application of U.S. patentapplication Ser. No. 16/557,175 filed Aug. 30, 2019 which claims benefitof Japanese Patent Application No. 2018-162607 filed on Aug. 31, 2018which are incorporated herein by reference.

BACKGROUND OF THE INVENTION 1. Field of the Invention

The present invention relates to a pattern inspection system using acharged particle beam device or the like, and more particularly, to apattern inspection system which executes machine learning based on imageinformation.

2. Description of the Related Art

In recent years, in a field of a semiconductor inspection or the like,an image analysis technology has been used, which extracts a featurevalue from an image, compares and collates the feature value withinformation registered in a database or the like in advance, anddetermines an object. A neural network or a support vector machine isknown as an algorithm of machine learning which determines an object. Inany method, identification accuracy largely varies depending on whichfeature value is selected, and thus, a selection method of the featurevalue is important.

In recent years, a deep learning device called Convolutional NeuralNetwork (CNN) has been developed and is attracting attention (AlexKrizhevsky, Ilya Sutskever, and Geoffrey E Hinton,“ImageNetClassification with Deep Convolutional Neural Networks”,Advances InNeural Information Processing Systems, Vol. 25, pp.1106-1114, 2012.). The CNN is a kind of machine learning device, inwhich a system automatically extracts and learns a feature of an image,and executes extraction of an object image included in the image,determination of an object, classification of the image, or the like. Ina support vector machine or the like of the related art, selection ofthe feature value required by machine learning can be automaticallyextracted from learning data, and thus, extremely high image analysisperformance is exerted.

However, in order to improve the image analysis performance by the CNN,it is necessary to exhaustively learn a variation of an analysis object,and thus, there is a problem that an operation is difficult in anapplication which requires time and effort to acquire the learning datalike the semiconductor inspection.

As a measure to expand the variation of the analysis object and reducethe data of the machine learning, there is Optical Proximity Correction(OPC) model creation. (Sun, Yuyang, et al. “Optimizing OPC data samplingbased on” orthogonal vector space“.” Optical Microlithography XXIV. Vol.7973. International Society for Optics and Photonics, 2011.). This is amodel which is used to simulate how a circuit design drawing of asemiconductor is formed on a silicon wafer through a semiconductormanufacturing apparatus, and executes the machine learning on arelationship between the circuit design drawing and an SEM photographactually manufactured on the wafer. In Sun, Yuyang, et al. “OptimizingOPC data sampling based on” orthogonal vector space“.” OpticalMicrolithography XXIV. Vol. 7973. International Society for Optics andPhotonics, 2011., in order to widen the variation of the analysisobject, a method is proposed in which circuit design data is referencedto analyze a variation of a circuit shape so as to determine a learningobject.

SUMMARY OF THE INVENTION

In a case where machine learning is used for an image inspection of asemiconductor, in order to cope with a variation of a photographingcondition of a Scanning Electron Microscope (SEM) used for imaging ofthe semiconductor, a variation of a circuit shape, a variation of afluctuation of a semiconductor manufacturing process, or a variation ofdeformation of the circuit shape due to a circuit formation position ona semiconductor device, it is necessary to prepare a lot of image data.In addition, it is necessary to create a correct inspection result(hereinafter, referred to as a true value) to be paired with each pieceof image data, and creating true values corresponding to a large amountof learning data requires manual work and a time. Furthermore, a largeamount of learning work using a calculator may take several weeks toseveral months and times. The learning work interferes with an operationof a production line, and thus, is difficult to be used. Therefore, amethod of selecting minimum data necessary to achieve target inspectionperformance is desired.

Accordingly, the present invention provides a pattern inspection systemcapable of shortening a learning time by saving time and effort on atrue value creation operation of learning data and reducing an amount ofthe learning data.

An aspect of the present invention provides a pattern inspection systemwhich inspects an image of an inspection target pattern of an electronicdevice using an identifier constituted by machine learning, based on theimage of the inspection target pattern of the electronic device and dataused to manufacture the inspection target pattern, the system including:a storage unit which stores a plurality of pattern images of theelectronic device and pattern data used to manufacture a pattern of theelectronic device; and an image selection unit which selects a learningpattern image used in the machine learning from the plurality of patternimages, based on the pattern data and the pattern image stored in thestorage unit.

Another aspect of the present invention provides a pattern inspectionsystem which inspects an image of an inspection target pattern of anelectronic device using an identifier constituted by machine learning,based on the image of the inspection target pattern of the electronicdevice and data used to manufacture the inspection target pattern, thesystem including: a storage unit which stores pattern data used tomanufacture a pattern of the electronic device and photographingcondition data of the image of the inspection target pattern; and aphotographing position selection unit which selects a photographingposition of a learning pattern image on the electronic device used inthe machine learning, based on the pattern data and the photographingcondition data stored in the storage unit.

Still another aspect of the present invention provides a patterninspection system which inspects an image of an inspection targetpattern of an electronic device using an identifier constituted bymachine learning, based on the image of the inspection target pattern ofthe electronic device and data used to manufacture the inspection targetpattern, the system including: a storage unit which stores pattern dataused to manufacture a pattern image of the electronic device and apattern of the electronic device and photographing condition data of theimage of the inspection target pattern; and an image selection unitwhich selects a learning pattern image used in the machine learning,based on the pattern data, the pattern image, and the photographingcondition data stored in the storage unit.

According to the present invention, it is possible to provide thepattern inspection system capable of shortening a learning time bysaving time and effort on a true value creation operation of learningdata and reducing an amount of the learning data.

Problems, configurations, and effects other than those described abovewill be apparent from a description of the embodiments below.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram illustrating an outline of a learning data selectionunit constituting an image generation unit according to an embodiment ofthe present invention;

FIG. 2 is a diagram illustrating an outline of an image photographingunit constituting the image generation unit according to the embodimentof the present invention;

FIG. 3 is a diagram illustrating outlines of a model generation unit anda model evaluation unit;

FIG. 4 is a flowchart illustrating a processing flow of the learningdata selection unit illustrated in FIG. 1;

FIG. 5 is a flowchart illustrating a detailed flow of a shape variationanalysis in Step S20 of FIG. 4;

FIG. 6 is a flowchart illustrating a detailed flow of avertical/horizontal edge ratio analysis in Step S201 of FIG. 5;

FIG. 7 is a flowchart illustrating a detailed flow of a pattern intervalanalysis in Step S202 of FIG. 5;

FIG. 8 is a flowchart illustrating a detailed flow of a pattern densityanalysis in Step S203 of FIG. 5;

FIG. 9 is a flowchart illustrating a detailed flow of learning dataselection processing by a shape variation in Step S30 of FIG. 4;

FIG. 10 is a flowchart illustrating a detailed flow of a positionvariation analysis in Step S40 of FIG. 4;

FIG. 11 is a flowchart illustrating a detailed flow of the learning dataselection processing by a position variation in Step S50 of FIG. 4;

FIG. 12 is a diagram illustrating a display screen example of a GUI of arecipe creation unit illustrated in FIG. 2;

FIG. 13 is a diagram illustrating a display screen example of a GUI of ateaching data creation unit illustrated in FIG. 3 and is a diagramillustrating a state when a true value assignment of learning data isexecuted;

FIG. 14 is a diagram illustrating an outline of an image generation unitaccording to the embodiment of the present invention;

FIG. 15 is a diagram illustrating an outline of an image generation unitaccording to another embodiment of the present invention;

FIG. 16 is a flowchart illustrating a processing flow of a learning dataselection unit illustrated in FIG. 15;

FIG. 17 is a flowchart illustrating a detailed flow of a processvariation analysis in Step S60 of FIG. 16;

FIG. 18 is a flowchart illustrating a detailed flow of a processvariation learning data selection in Step S70 of FIG. 16;

FIG. 19 is an overall schematic configuration diagram of a semiconductormeasurement system;

FIG. 20 is a schematic configuration diagram of a scanning electronmicroscope;

FIGS. 21A to 21D are schematic diagrams for explaining a calculation ofvertical and horizontal edge pixels of a pattern, FIG. 21A illustrates avertex coordinate in design data, FIG. 21B illustrates a design dataimage, FIG. 21C illustrates an edge image, and FIG. 21D illustrates thenumbers of pixels of the vertical edge and the horizontal edge; and

FIG. 22 is an overall schematic configuration diagram of the imagegeneration unit and an evaluation device.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

An object of an image generation unit constituting a pattern inspectionsystem illustrated in embodiments described below is to reduce an amountof learning data in a semiconductor inspection using machine learningand shorten a learning time. Moreover, as a specific example thereof, anexample of generating an image data set for learning using design dataand an SEM photographing condition will be illustrated.

In the present specification, an apparatus having a function ofgenerating learning data in the semiconductor inspection using themachine learning, that is, the pattern inspection system will bedescribed. For example, a charged particle beam device used as thepattern inspection system includes a focused ion beam (FIB) device whichscans a sample with an ion beam to form an image, a scanning electronmicroscope (SEM) which scans a sample with an electron beam to form animage, a scanning electron microscope (Critical Dimension-ScanningElectron Microscope: CD-SEM) for a length measurement which is a type ofmeasurement apparatus, or the like. However, in order to measure apattern progressing with miniaturization with high accuracy, anextremely high magnification is required, and thus, it is desirable touse the SEM which surpasses the FIB device in terms of resolution ingeneral.

FIG. 19 is an overall schematic configuration diagram of a semiconductormeasurement system, and is a schematic explanatory diagram of ameasurement/inspection system in which a plurality of measurement orinspection devices are connected to a network. In the semiconductormeasurement system illustrated in FIG. 19, a CD-SEM 2401 (also referredto as a length measurement SEM) which mainly measures a patterndimension of a semiconductor wafer, a photo-mask, or the like, and adefect inspection device 2402 which acquires an image by irradiating asample with an electron beam and extracts a defect based on a comparisonbetween the image and a reference image registered in advance areconnected to a network. In addition, a condition setting device 2403which sets a measurement position, a measurement condition, or the liketo design data of a semiconductor device, a simulator 2404 whichsimulates performance of a pattern based on a manufacturing condition orthe like of a semiconductor manufacturing apparatus, and a storagemedium 2405 in which layout data of the semiconductor device or thedesign data in which the manufacturing condition is registered is storedare connected to the network.

For example, the design data is expressed in a GDS format, an OASISformat, or the like and is stored in a predetermined format. Inaddition, the design data can be of any type as long as software whichdisplays the design data can display the format and can be handled asgraphic data. In addition, the storage medium 2405 may be built in ameasurement device, a controller of an inspection device, the conditionsetting device 2403, or the simulator 2404. In addition, the CD-SEM 2401and the defect inspection device 2402 are provided with respectivecontrollers, and thus, a control necessary for each device is executed.However, the controller may be equipped with a function of the simulator2404 or a setting function such as a measurement condition.

In the SEM, electron beams emitted from an electron source are focusedby a plurality of stages of lenses, and the sample is scanned with thefocused electron beams one-dimensionally or two-dimensionally by ascanning deflector. Secondary electrons (SE) or backscattered electrons(BSE) emitted from the sample by the scanning of the electron beam aredetected by a detector and is stored in the storage medium such as aframe memory in synchronization with the scanning of the scanningdeflector. An image signal stored in this frame memory is integrated byan arithmetic device mounted in the controller. In addition, thescanning by the scanning deflector is possible for any size, position,and direction.

The control as described above is executed by the controller of eachSEM, and an image or signal obtained by a result of the scanning of theelectron beam scan is sent to the condition setting device 2403 via acommunication line network. In addition, in the present example, thecontroller which controls the SEM and the condition setting device 2403are described separately. However, the present invention is not limitedto this, the device control and the measurement processing may becollectively executed by the condition setting device 2403, and thecontrol of the SEM and the measurement processing may be executedtogether in each controller.

Moreover, a program for executing measurement processing is stored inthe condition setting device 2403 or the controller, and a measurementor calculation is executed according to the program.

Further, the condition setting device 2403 has a function of creating aprogram (a recipe), which controls the operation of the SEM, based onthe design data of the semiconductor, and functions as a recipe creationunit. Specifically, by setting a position for executing processingrequired for the SEM such as a desired measurement point, auto focus,auto stigma, or an addressing point on the design data, contour linedata of a pattern, or design data subjected to the simulation, a programfor automatically controlling a sample stage of the SEM, a deflector, orthe like is created, based on the setting.

Hereinafter, with reference to the drawings, an embodiment will bedescribed with a CD-SEM (also referred to as the length measurement SEM)as an example of the charged particle beam device.

First Embodiment

FIG. 20 is a schematic configuration diagram of the scanning electronmicroscope. An electron beam 2503 which is extracted from an electronsource 2501 by an extraction electrode 2502 and is accelerated by anacceleration electrode (not illustrated) is narrowed by a condenser lens2504 which is a form of a focusing lens, and then, a sample 2509 isscanned with the electron beam 2503 one-dimensionally ortwo-dimensionally by a scanning deflector 2505. The electron beam 2503is decelerated by a negative voltage applied to an electrode built in asample stage 2508 in a chamber 2507 and is focused by a lens action ofan objective lens 2506 to irradiate the sample 2509.

If the sample 2509 is irradiated with the electron beam 2503, anelectron 2510 including a secondary electron and a backscatteredelectron are emitted from an irradiation location. The emitted electron2510 is accelerated in an electron source direction by an accelerationaction based on the negative voltage applied to the sample, collideswith a conversion electrode 2512, and generates a secondary electron2511. The secondary electron 2511 emitted from the conversion electrode2512 is captured by a detector 2513 and an output I of the detector 2513is changed depending on an amount of the captured secondary electrons. Aluminance of a display device (not illustrated) is changed depending onthe output I. For example, in a case where a two-dimensional image isformed, an image of a scanning area is formed by synchronizing adeflection signal to the scanning deflector 2505 and the output I of thedetector 2513 with each other. In the example illustrated in FIG. 20, anexample is described in which the electron 2510 emitted from the sample2509 is subjected to one-end conversion by the conversion electrode 2512for detection. However, of course, the present invention is not limitedto the configuration, and it is also possible to dispose a detectionsurface of an electron multiplying tube or the detector on a trajectoryof the accelerated electron. A controller 2514 has a function whichcontrols each configuration of the scanning electron microscope and alsoforms an image based on the detected electron, and a function whichmeasures a pattern width of a pattern formed on the sample based on anintensity distribution of the detected electron referred to as a lineprofile.

Next, the overall schematic configuration diagram of generation andevaluation devices of learning image data of the machine learning andevaluation device is illustrated in FIG. 22. Design data 101 and an SEMphotographing condition 102 are input to an image generation unit 1, andthe learning image data is generated and is stored in a learning imagedata storage unit 203. A model generation unit 30 learns the learningimage data and generates a model 303 for executing the image inspection.A model evaluation unit 40 extracts image data from an evaluation imagedata storage unit 401 using the model 303, executes evaluation, andgenerates an evaluation result 402. As illustrated by dotted lines inFIG. 22, the evaluation device includes the design data 101, the SEMphotographing condition 102, the image generation unit 1, the learningimage data storage unit 203, the model generation unit 30, the model303, the model evaluation unit 40, and the evaluation image data storageunit 401.

One aspect of each of the image generation unit 1, the model generationunit 30, and the model evaluation unit 40 will be described. The imagegeneration unit 1, the model generation unit 30, and the modelevaluation unit 40 can be executed by an arithmetic device which isbuilt in the controller 2514 or has an image processing function, or canexecute the image generation by an external arithmetic device (forexample, condition setting device 2403) via a network.

FIG. 1 is a diagram illustrating an outline of the learning dataselection unit constituting the image generation unit 1 according to anembodiment of the present invention. That is, FIG. 1 is a diagramexplaining an example of learning data selection unit 103 which createsan SEM photographing coordinate list, in order to generate the learningimage data of the machine learning. The learning data selection unit 103uses the design data 101 and the SEM photographing condition 102 toobtain a coordinate of the photographing position in order to acquire animage suitable for learning and outputs the coordinate as a learningimage coordinate list 104.

FIG. 4 is a flowchart illustrating a processing flow of the learningdata selection unit 103 illustrated in FIG. 1. In a case where themachine learning is applied to the image inspection, it is necessary toexhaustively learn a variation of a subject which becomes an inspectiontarget. In this example, an example of efficiently selecting thelearning data so as to cover the variation of the pattern shape on thesemiconductor device using the design data of the semiconductor deviceand the photographing conditions of the SEM will be described. In thecase of a semiconductor, an amount of deformation of a pattern shapevaries depending on a formation position of the pattern on thesemiconductor device due to a fluctuation of a manufacturing process,and thus, a selection in consideration of the pattern formation positionis effective in addition to the variation of the pattern shape.Hereinafter, after the outline is described, details of each processingwill be described.

As illustrated in FIG. 4, in Step S10, first, the learning dataselection unit 103 executes field of view (FOV: also referred to as animaging field of view) design data clipping processing. That is, thedesign data 101 is clipped at a position on the design data 101corresponding to the SEM photographing condition 102 and by an imagesize to be acquired. For example, in a case where the FOV is 2 μm atphotographing positions X and Y, the design data 101 is clipped with asize of 2 μm about X and Y coordinates. After the design data 101 isdrawn in advance, the image of the design data 101 may be clipped, orafter a clip region is obtained, the design data corresponding to theclip region may be drawn.

Sequentially, in Step S20, the learning data selection unit 103 executesshape variation analysis processing. That is, features relating to theshape of the pattern and the density of the pattern are determined fromthe design data image clipped in Step S10.

Moreover, in Step S30, the learning data selection unit 103 executesshape variation learning data selection processing. That is, thelearning data selection unit 103 selects one or more patterns suitablefor the learning data, using an index obtained from the shape of thepattern and the density of the pattern obtained in Step S20.

Next, in Step S40, the learning data selection unit 103 executesposition variation analysis processing. That is, the pattern of the sameshape of the pattern selected in Step S30 is detected from the designdata 101. In the position variation analysis processing, coordinatepositions of one or more isomorphic patterns are obtained.

In Step S50, the learning data selection unit 103 executes positionvariation learning data selection processing. That is, the learning dataselection unit 103 selects a pattern suitable for the learning datausing the coordinate position of the isomorphic pattern obtained in StepS40.

The above processing is executed on all local design data clipped fromthe design data 101. In a case where the FOV size is small due to alimitation of the photographing condition of the SEM, analysis of alarge amount of clipped design data is required, and thus, the number ofanalyzes may be limited. For example, a preset number of coordinates maybe randomly selected, and only design data corresponding to thecoordinates may be limited and analyzed, or coordinates obtained bysampling design data at predetermined intervals may be selected, andonly the design data corresponding to the coordinates may be limited andanalyzed.

FIG. 5 is a flowchart illustrating a detailed flow of the shapevariation analysis in Step S20 of FIG. 4. In Step S201, the learningdata selection unit 103 executes vertical/horizontal edge ratio analysisprocessing. That is, the learning data selection unit 103 calculates aratio between the vertical edge and the horizontal edge of the patternusing the design data image. Since the design data is mainly arectangular pattern in a vertical direction and a horizontal direction,a tendency of the pattern shape can be ascertained from the ratio of thevertical edge and the horizontal edge.

In the following Step S202, the learning data selection unit 103executes pattern interval analysis processing. That is, the learningdata selection unit 103 calculates a width and an interval of thepattern from the design data image.

Moreover, in Step S203, the learning data selection unit 103 executespattern density analysis processing. That is, the learning dataselection unit 103 calculates the density of the pattern using thedesign data image created from the design data. The pattern density canbe calculated using the number of patterns, an area of the pattern, orthe like.

FIG. 6 is a flowchart illustrating a detailed flow of thevertical/horizontal edge ratio analysis in Step S201 of FIG. 5. Asillustrated in FIG. 6, in Step S2011, vertical/horizontal edge componentdetection processing of detecting the number of vertical edges andhorizontal edges from the design data is executed.

In Step S2012, vertical/horizontal edge pixel number count processing ofcounting the number of pixels of the vertical/horizontal edge componentdetected in Step S2011 is executed.

In Step S2013, vertical/horizontal edge ratio calculation processing ofcalculating a ratio of vertical/horizontal edges from thevertical/horizontal edge pixels counted in Step S2012 is executed.

Here, FIGS. 21A to 21D are schematic diagrams for explaining acalculation of vertical and horizontal edge pixels of the pattern fromthe design data. Based on vertex coordinates A, B, C, and D of a closedfigure which is information of the design data illustrated in FIG. 21A,the design data image illustrated in FIG. 21B can be created. In thisexample, since there is a pattern in the closed figure, the inside ofthe closed figure is painted in black, and the outside of the closedfigure without the pattern is painted in white. By executing edgedetection on the design data image illustrated in FIG. 21B, an edgeimage illustrated in FIG. 21C can be obtained. This corresponds to thevertical/horizontal edge component detection processing in Step S2011illustrated in FIG. 6. By separately counting the number of edge pixelsincluded in a vertical line and the number of edge pixels included in ahorizontal line from the edge image illustrated in FIG. 21C, it ispossible to obtain the number of pixels of the vertical edge and thehorizontal edge illustrated in FIG. 21D. This corresponds to thevertical/horizontal edge pixel number counting process in Step S2012illustrated in FIG. 6. The ratio of vertical/horizontal edges can becalculated using the number of pixels of the vertical/horizontal edges.

FIG. 7 is a flowchart illustrating a detailed flow of a pattern intervalanalysis in Step S202 of FIG. 5. First, in Step S2021, the design dataimage is searched by row/column sampling in a row direction (xdirection) and a column direction (y direction) to detectpresence/absence of the pattern, and the width of the pattern and theinterval thereof are calculated and stored. The search may be performedfor each row and each column, or the processing time may be shortened byskipping every several rows and several columns.

In Step S2022, pattern interval maximum/minimum value/average valuedetection processing is executed. That is, in Step S2021, the maximumvalue, the minimum value, and the average value are calculated based onthe width and the interval of the pattern stored by the row/columnsampling.

FIG. 8 is a flowchart illustrating a detailed flow of the patterndensity analysis in Step S203 of FIG. 5. In Step S2031, the design dataimage is generated from the design data, and image grid settingprocessing of setting a Grid to the design data image is executed.

In Step S2032, the number of Grids including the patterns is counted,and in Step S2033, the pattern densities of all Grids are calculated.

FIG. 9 is a flowchart illustrating a detailed flow of the learning dataselection processing by the shape variation in Step S30 of FIG. 4. Inthe shape variation learning data selection processing, the learningdata (sample) in consideration of the shape variation is selected usingthe ratio of vertical/horizontal edges, the pattern interval, and avalue of the pattern density.

As illustrated in FIG. 9, first, in Step S301, the learning dataselection unit 103 executes shape clustering processing and clusters theshape indices using the ratio of vertical/horizontal edges, the patterninterval, and the value of the pattern density, and thus, divides theshape indices into one or more classes are divided. For example, amethod of the clustering can be realized by a known technique such as ak-means method. Subsequently, in Step S302, the learning data selectionunit 103 selects n (n≥1) samples from each cluster. It is desirable toselect a sample suitable for the learning data so as to be exhaustivelyunbiased with respect to the shape or density of the pattern. Therefore,preferably, the sample is selected so that a difference between theratio of vertical/horizontal edges, the pattern interval, and the valueof the pattern density is large between the samples, so as not to bebiased toward the sample having the same tendency. In a case where asufficient number of samples can be selected, the sample may be selectedrandomly. For example, in a case where the number of the samples issmall, the sample may be selected so that the difference between theratio of vertical/horizontal edges, the pattern interval, and the valueof the pattern density is large between the samples, and in a case wherethe number of samples to be selected is large, the sample may berandomly selected. That is, the selection method may be switcheddepending on the number of samples. The number of samples may bedetermined in advance by default, or may be set by a user. The number ofsamples may be determined using a conversion table based on statisticalprocessing or an experimental value using the ratio ofvertical/horizontal edges, the pattern interval, and the value of thedensity in pattern variation analysis processing of the design data.

FIG. 10 is a flowchart illustrating a detailed flow of the positionvariation analysis in Step S40 of FIG. 4. In the position variationanalysis processing, the design data is searched based on the shapes ofall the samples selected in the shape variation learning data selectionprocessing in Step S30 so as to detect an isomorphic pattern, and theposition coordinate thereof is stored.

Specifically, as illustrated in FIG. 10, when the pattern selected inStep S30 is indicated by set A and the number of the patterns selectedin Step S30 is indicated by N, i is set to 1, and in Step S401, thelearning data selection unit 103 determines whether or not i≤N issatisfied. As a result of the determination, if i exceeds N, theprocessing ends. Meanwhile, as the result of the determination, if i≤N,the step proceeds to Step S402.

In Step S402, the learning data selection unit 103 extracts the i-thpattern Pi from the set A, and the step proceeds to Step S403.

In Step S403, the learning data selection unit 103 searches a positionof a pattern similar to the pattern Pi from a design drawing (designdata), stores the position in a set Bi, and returns to Step S401 so asto repeat the processing.

Moreover, the detection of the isomorphic pattern can be realized bytemplate matching which uses the image of the sample as a template. In acase where a similarity of an image obtained by a known technique suchas a normalized correlation is higher than a specific threshold value,the position coordinate is stored as the isomorphic pattern. It is alsoconceivable to determine the detection position assumed on a wafer. Aplurality of Chips are generated on the wafer based on the design data.The Chips have the same circuit pattern. Accordingly, for example, evenin a case where there is no similar pattern on the design data of theChip, the plurality of Chips are generated on the wafer, and thus, theremust be a plurality of isomorphic patterns, and it is also conceivableto store the detection position of the isomorphic pattern assuming them.In other words, in the circuit patterns (the circuit patterns of allChips are the same as each other) of the plurality of Chips formed onthe wafer, the feature shape (feature value) of the circuit pattern inthe FOV, from among the circuit patterns of the respective Chips, doesnot exist in the other circuit patterns in the Chips by the setting ofthe FOV, and the feature shape (the feature value) is the same as eachother in the circuit patterns of all the Chips formed on the wafer.

FIG. 11 is a flowchart illustrating a detailed flow of the learning dataselection processing by the position variation in Step S50 of FIG. 4. InStep S501, the learning data selection unit 103 clusters data of aplurality of position coordinates where the isomorphic pattern storedfor each pattern exists, and divides the data into one or more classes.The clustering can be realized in the same manner as Step S301 describedabove. Subsequently, in Step S502, m (m≥1) samples are randomly selectedfrom the divided classes. Moreover, an image coordinate of the selectedsample is output as the learning image coordinate list 104.

When a pattern is transferred to a wafer, the pattern shape is changedin a process fluctuation even if the pattern is an isomorphic pattern ondesign data. Therefore, in the position variation learning dataselection processing (Step S50), the fluctuation of the generatedpattern shape is added as the learning data, and thus, data having adifferent position coordinate in the isomorphic pattern is selected.

In this method, it is possible to obtain a sample suitable for thelearning which contributes to generalization performance by eliminatinga bias in the pattern shape by the shape variation analysis processing(Step S20) and the shape variation learning data selection processing(Step S30). In addition, by means of the position variation analysisprocessing (Step S40) and the position variation learning data selectionprocessing (Step S50), it is possible to create the learning data whichcontributes to the generation of a robust identifier with respect to theshape fluctuation which actually occurs. Here, for example, it isconceivable to execute the learning by adding the learning datasubjected to image processing such as distortion of the pattern shape byaugmentation and to generate the identifier robust the shapefluctuation. However, in a case where the distortion of the added shapeis different from the degree of distortion which actually occurs, theidentification performance may deteriorate.

Meanwhile, in the present method, since the fluctuation of the actuallygenerated shape is added as the learning data, the identificationperformance can be stably improved.

Next, an image photographing unit 20 will be described. FIG. 2 is adiagram illustrating an outline of the image photographing unit 20constituting the image generation unit 1 according to the embodiment ofthe present invention. As illustrated in FIG. 2, the image photographingunit 20 creates a recipe to photograph the SEM by a recipe creation unit201 from the learning image coordinate list 104, photographs the SEM bya photographing unit 202 based on the created recipe, and stores thephotographed SEM in the learning image data storage unit 203. Here, itis conceivable to display the recipe creation unit 201 on the displayscreen of the GUI as illustrated in FIG. 12 so that the user can confirmthe recipe creation unit 201. The design figure based on the design dataand the coordinates may be displayed in correspondence with each other,or the coordinate displayed according to the users instruction may beadded to or deleted from the learning image coordinate list 104.

Subsequently, the model generation unit 30 will be described. FIG. 3 isa diagram illustrating outlines of the model generation unit 30 and themodel evaluation unit 40. For the learning image which is stored in thelearning image data storage unit 203, the model generation unit 30causes the user to give corresponding teaching data in the teaching datacreation unit 301 for each pixel or for each image. In addition, alearning unit 302 learns a relationship between the learning image andteaching data corresponding to the learning image to create the model303. The learning unit 302 can be realized by an identifier used in themachine learning. For example, the learning unit 302 can be realized byan identifier based on a multi-layered neural network (that is, CNN)such as deep learning.

As a case where the teaching data for each pixel is required, there is asemantic segmentation by the deep learning. In this task, a label isattached to each pixel of the image. This label means a type of thepixel. The learning unit 302 learns a model which estimates the labelfor each pixel from the image. For example, in a case where anidentifier model for the semiconductor inspection so as to extract thecontour line of the circuit pattern from the SEM image of the circuitpattern is created, as an example of the teaching data, teaching data ofthe image in which the label of an area division is expressed as an8-bit signal of RGB is created, and in the area division, the pixel ofthe contour line is red, and other pixels are blue. The teaching datamay be created in which the inside, the outside, and the contour line ofthe pattern are divided and color-coded. In a case where the circuitpattern which is the object crosses a plurality of layers, teaching datamay be created, in which the inside, the outside, and the contour lineof each layer pattern are finely color-coded. In this case, a learningimage based on the learning image coordinate list 104 is displayed onthe GUI, and teaching data is superimposed on the image and createdwhile visually being confirmed. Moreover, in a case where detailedteaching data of the contour line or the like is created, it isdesirable to create the teaching data using a pen tablet. In a casewhere the inside, the outside, and the contour line of the pattern foreach layer in the plurality of layers are divided and color-coded, it isnecessary to determine each color. In a true value assignment of a dataset used for learning, the colors corresponding to attributions such asthe number (indicating what layer it is) from an upper layer, the insideof the pattern, the outside of the pattern, the contour line, or thelike are determined such that all data is unified. In addition, even ifthere are different data sets, there is a possibility that they will belearned together later. In addition, it is not limited to the data set,and for example, in a data set for evaluating the identifier whichextracts the contour line, it is desirable to match the correspondencesbetween the attributions and the colors in all the data sets. In thiscase, by using design data corresponding to the SEM image of thelearning data, it is possible to obtain the number of attributions andthe types of the attributions (the number of layers, the inside of thepattern, the outside of the pattern, and the contour line of thepattern) required for the true value assignment of the SEM image. As forthe number of attributions, if it is the SEM image of a single-layerpattern, there are three attributions of the inside of the pattern, theoutside of the pattern, and the contour line of the pattern. Moreover,if it is the SEM image of a two-layer pattern, in first and secondlayers, there are six attributions in the inside of the pattern, theoutside of the pattern, and the contour line of the pattern, and thenumber of attributions is three times the number of layers. For example,if inner and outer boundaries of the pattern are considered as thecontour lines and the true value assignment is applied to only theinside and the outside of the pattern, the number of attributions is twotimes the number of layers. The color corresponding to the number of theattributions is considered to be arbitrarily determined by the user, andit is desirable to previously determine the color corresponding to thenumber of the attribution according to the type of the attribution. Inthis case, the color may be randomly determined, a vivid color may bechosen in a color space such that each color is easily seen, each colormay be chosen such that a distance is even in the color space, or thecolor may be determined such that the distance is the greatest. Inaddition, it is conceivable that an estimation true value image with acolor corresponding to each attribution is created with the inside(pattern region) of the pattern, the outside (non-pattern region) of thepattern, and the boundary as the contour line of the pattern, based onthe design data. It is also conceivable that the created estimation truevalue image is displayed on the display screen, and the user executesthe true value assignment with reference to this estimation true valueimage. In this time, a palette of the colors of all the attributions ofthe estimation true value image is displayed, and for example, byspecifying the color of the palette, a color of a pen of a pen tabletmay be the color of the palette.

It is also conceivable to display the estimation true value imagedisplayed on the display screen so as to be superimposed on the SEMimage to be subjected to the true value assignment, and process theestimation true value image so as to create a true value image. In thiscase, it is conceivable that the user moves a portion or one point ofthe boundary (the contour line) between the pattern and the non-patternof the estimation true value image to an ideal position of the contourline by the pen table while looking at the SEM image displayed to besuperimposed. It is conceivable that a portion of the moving contourline or a point of another contour line continuous to one point is alsomoved according to the movement. An amount of the movement decreases asthe distance from a portion or one point of the contour line instructedby the user increases. In addition, it is conceivable that the boundarybetween the pattern region and the non-pattern region is also changedaccording to the moving contour line.

Moreover, in addition to the design data, it is conceivable that thetype of attribution and the color corresponding to the number ofattributions are determined by process information. In this case, theestimation true value image is created using the design data and theprocess information. In this case, it is conceivable to separatelymanage the color of the attribution of a structure such as avia-in-trench in which a via exists in a trench.

In addition, for the true value image data created by the user, afunction for thickening or thinning the contour line is provided, andfor example, it is conceivable to thicken the contour line of one pixeldrawn by the user to three pixels or thin the contour lines of tenpixels to five pixels, depending on the users instruction.

Moreover, it is also conceivable to estimate the contour lines of theSEM image from the design data by simulation in the true value datacreation and perform the true value assignment based on the estimation.

Moreover, it is also conceivable to add a mask area as an area outsidethe learning object. In addition, in a case where a region becoming anidentification target is small, the identification performance isimproved by changing learning weight. Therefore, in a case where thereis a large difference in the type of attribution area which analyzes andidentifies the entire data set of true value data created by the user,the learning weight of each attribution is changed depending on thedifference. For example, in a case where a ratio of three types ofattribution areas in the entire data set is 100:100:1, it is conceivableto change the learning weight of each attribution to 1:1:100.

Here, the entire data set of the true value data created by the user isanalyzed. However, it is conceivable that the ratio of the area of eachattribution is similarly obtained by analyzing the design data of theentire data set to change the learning weight of each attribution.Moreover, it is conceivable that the user sets the learning weight ofeach attribution empirically.

In addition, in generation of a defect identifier, it is conceivablethat the user color-codes a defect region in the image, performs truevalue assignment, and learns to create an identifier for detecting thedefect region included in the image. In this case, the defect region ora normal region may be color-coded to perform the true value assignment.Hereinbefore, the example which creates a label as a 24-bit signal ofRGB is described. However, as long as it is the information in which theidentifier can recognize a label, the present invention is not limitedto this.

As a case where the teaching data is required for each image, there isan image classification by the deep learning. In this task, a type ofimage is selected for each image, and a label indicating the type isgiven as teaching data. A model, which estimates the type of the imagefrom the image, is learned by the learning unit 302. For example, in acase where the model of the identifier which classifies a defect imageis learned, the teaching data is created in which information of defecttype is tagged for each image. In this case, the learning image based onthe learning image coordinate list 104 obtained by the learning dataselection unit 103 is displayed on the display screen of the GUI, andthe teaching data of the defect type is created while the defect type isvisually confirmed.

Here, it is conceivable that the generated model of the identifier isdivided into a single layer, a multilayer, or the like. In this case, itis conceivable to select the model of the identifier using the designdata. Moreover, when a learning data set is generated, the learning dataset may be divided into a single layer, a multilayer, or the like usingthe design data so as to generate the learning data set.

In addition, similarly, it is conceivable that the learning data set isgenerated using the process information and the model of the identifieris selected using the process information.

Moreover, it is conceivable to divide the model of the identifieraccording to a photographing magnification of the SEM image and a frameintegration number of the photographing. In this time, it is conceivableto generate the learning data set and select the model using the SEMphotographing condition information.

A management of the created model of the identifier creates a modelmanagement table indicating a model name and the type of thecorresponding model. The type of the model includes the number of layersacquired from the design data corresponding to the learning data set, aprocess acquired from the process information, the photographingmagnification acquired from SEM photographing condition information, theframe integration number, or the like. In addition to these, informationthat the user wants to add may be included in the type of the model.

When the image data is identified, based on the design data of the imagedata, the process information, the photographing condition information,and a model management table, it is confirmed whether the model is anapplicable model, and in a case of different types of models whichcannot be applied, a function of notifying the user to that effect maybe provided.

In addition, it is also conceivable to perform identification bysearching for a most suitable model among a plurality of models based onthe design data of the image data to be identified, the processinformation, the SEM photographing condition information, and the modelmanagement table.

Hereinbefore, the learning image coordinate list 104 is created from thedesign data 101 and the SEM photographing condition 102, thephotographing is performed based on the coordinates, the true valueassignment is performed on the obtained image data, and the learningimage and teaching data are created.

However, there are cases where it is desirable to select the learningimage from the photographed SEM image. Therefore, the outline of theimage generation unit 1 for selecting the learning image from the imagedata already photographed is illustrated in FIG. 14. However, this imagegeneration unit 1 is premised on a presence of the design data 101 andthe SEM photographing condition 102 corresponding to the image dataalready photographed. The inputs are the design data 101 and SEMphotographing condition 102, the learning data is selected by thelearning data selection unit 103 while these are input, and image datacorresponding thereto is extracted from a photographed image datastorage unit 204 and stored in the learning image data storage unit 203.The learning data selection unit 103 used here can be realized bysubstantially the same processing as the above-described learning dataselection unit 103. Differences therebetween are that the image to beclipped, the pattern shape to be analyzed, and the coordinate positionare limited to the shapes and position coordinates corresponding to thealready photographed image. For example, the FOV design data clippingprocessing in Step S10 of FIG. 4 can be omitted because there is alreadythe clipped design data 101. The following can be realized by limitingthe shape and position coordinate corresponding to the alreadyphotographed image.

The pattern shape generated by the process fluctuation, such as adeviation of an optimal exposure condition, is changed. It is consideredthat the pattern shape can be grasped to some extent by changing a widthof a white band of the pattern and a degree of a roughness also from theSEM image obtained by photographing the pattern.

As described above, according to the present embodiment, it is possibleto provide the pattern inspection system capable of shortening thelearning time by saving time and effort on the true value creationoperation of the learning data and reducing the amount of the learningdata.

Moreover, according to the present embodiment, it is possible to shortenthe learning time while maintaining the accuracy of the model 303 (FIG.22).

Second Embodiment

FIG. 15 is a diagram illustrating an outline of an image generation unitaccording to another embodiment of the present invention. The presentembodiment is different from the first embodiment in that the learningdata is selected by the learning data selection unit 103 based on inputsfrom the design data 101 and the photographed image data storage unit204. The same reference numerals are assigned to the same components asthose of the first embodiment, and hereinafter, repeated descriptionsare omitted.

First, in the present embodiment, in a case where there is alreadyacquired image data, the present embodiment is focused on the fact thatit is possible to select the learning data using the information of theprocess variation that has actually occurred from the image data. Asillustrated in FIG. 15, the inputs are the design data 101 and thephotographed image data storage unit 204, the learning data is selectedby the learning data selection unit 103 while these are input, and theimage data is stored in the learning image data storage unit 203.

FIG. 16 is a flowchart illustrating a processing flow of the learningdata selection unit 103 illustrated in FIG. 15. Shape variation analysisprocessing in Step S20 and shape variation selection processing in StepS30 are the same as those in FIG. 4 of the above-described firstembodiment, and descriptions thereof are omitted. Sequentially, in StepS60, the learning data selection unit 103 executes process variationanalysis processing. That is, for the SEM image corresponding to thepattern selected in Step S30, the variation due to the processfluctuation is analyzed.

Subsequently, in Step S70, the learning data selection unit 103 executesthe process variation learning data selection processing. That is, thelearning data is selected based on an evaluation value of the processvariation analyzed in Step S60.

FIG. 17 is a flowchart illustrating a detailed flow of the processvariation analysis in Step S60 of FIG. 16. In Step S601, it is knownthat the learning data selection unit 103 changes an inclination of aside wall of the pattern depending on the pattern shape or the patternportion due to the fluctuation of the process, and it is conceivablethat the width of the white band of the pattern is changed when the sidewall of the pattern is changed. Therefore, white band width variationdetection processing is performed to detect a white band width variationas an index for evaluating the variation due to the process fluctuation.The white band width can be detected by calculating the variation of thewidth of the white region by threshold value binarization in which noiseremoval is performed on the SEM image by Gaussian filtering or the likeand the white band is set to white.

Subsequently, in Step S602, the learning data selection unit 103executes pattern inside/outside determination processing. That is, thepattern (the inside of the pattern) and the portion other than thepattern (the outside of the pattern) are determined. In this case,alignment can be executed on the SEM image and the design data bypattern matching to detect the inside and the outside of the pattern.Further, the noise removal is performed on the SEM image by Gaussianfiltering or the like by utilizing a difference in density between theinside and outside of the pattern, the binarization is realized, and itis possible to determine the inside and outside of the pattern.

Subsequently, in Step S603, the learning data selection unit 103executes pattern inside luminance variation detection processing. Thatis, the variation of a luminance value of a region inside the pattern isdetermined.

Subsequently, in Step S604, the learning data selection unit 103executes pattern outside luminance variation detection processing. Thatis, the variation of a luminance value of a region outside the patternis obtained.

Here, in the white band width variation detection processing (StepS601), the pattern inside luminance variation detection processing (StepS603), the pattern outside luminance variation detection processing(Step S604), the variation of luminance value is obtained. However,instead of the variation of the luminance value, a maximum value and aminimum value of the luminance value may be determined. Further, it maybe configured to obtain a variation of roughness, a variation of noiseinside and outside the pattern, or the like.

FIG. 18 is a flowchart illustrating a detailed flow of the processvariation learning data selection in Step S70 of FIG. 16. In Step S701,the learning data selection unit 103 performs clustering based on thevalues of the variation of the white band width, the variation of theluminance value of the pattern inside region, and the variation of theluminance value of the pattern outside region detected by the processvariation analysis processing (Step S60) for each image of theisomorphic pattern of the pattern shape selected by the shape variationlearning data selection processing (Step S30), and divides theclustering into one or more classes. In addition, the clustering can berealized in the same manner as that of Step S301 described above.Subsequently, in Step S702, m (m≥1) pieces of image data are randomlyselected from the divided classes. Moreover, the selected image data isstored in the learning image data storage unit 203.

Here, for the SEM image, the variation due to the process fluctuation isanalyzed to select the image. However, from the photographing positionof the photographing information obtained by photographing the SEMimage, similarly using the position variation analysis processing (StepS40) described above, the position on the design data, the position onthe semiconductor device, and the position on the wafer are determined,and an image where the variation of the process fluctuation looks largeis selected. For example, selecting an image of patterns ofphotographing positions corresponding to vicinities of a center and fourcorners on the semiconductor device, selecting an image of patterns ofphotographing positions corresponding to vicinities of a center and fourcorners on a wafer, selecting an image of patterns of photographingposition corresponding to a position on the wafer where heat fromexposure is the highest and a position on the wafer where the heat isthe lowest in consideration of lens heating, selecting a distancebetween the images of the pattern of photographing position to be thelongest, or the like is considered.

In addition, for the SEM image of the isomorphic pattern of the patternshape selected in the shape variation learning data selection processing(Step S30) using the design data, using the photographing information ofthe SEM image, the SEM image of the pattern of the photographingposition is selected using the position variation analysis processing(Step S40) described above from the SEM image photographing position,and thereafter, for the selected SEM image, the variation due to theprocess fluctuation is analyzed from the variation of the white bandwidth, the variation of the luminance value of the pattern insideregion, the variation of the luminance value of the pattern outsideregion, or the like, and the image in which the variation of the processfluctuation looks large may be carefully selected.

Here, the image data of the photographed image data storage unit 204(FIG. 15) may be image data obtained by randomly determining andphotographing the photographing position in advance, or may be imagedata obtained by photographing a wafer having photographing positionswhile being evenly spaced. Moreover, it is also conceivable to use aFocus Exposure Matrix (FEM) wafer in which a circuit pattern isgenerated by changing the exposure conditions. In this case, it is alsoconsidered that the photographing position is a photographing positionwhere images of different exposure conditions can be photographed. Inthis case, the exposure condition corresponding to the photographingposition is known, and thus, the variation of the exposure conditionwhich is one factor of the process fluctuation can be obtained based onthe photographing position. In this case, it is desirable to select theimage so that a difference in exposure conditions between the imagesincreases.

Compared to the configuration of the first embodiment, according to theabove-described present embodiment, the learning data is selected by thelearning data selection unit 103 based on the inputs from the designdata 101 and the photographed image data storage unit 204, and thus, itis possible to more shorten the learning time.

A portion or all of the processing in the above-described firstembodiment or second embodiment may be created by a processing programoperated by a general-purpose CPU. In addition, it is also conceivableto execute a portion of all of the above-described processing by adedicated LSI or FPGA. The above-described design data 101 may be adesign drawing of the circuit pattern created by a designer, may be apattern shape calculated from the SEM image, or may be a pattern shapecreated by simulation.

The present invention is not limited to the above-described embodiments,but includes various modification examples. For example, the embodimentsare described in detail in order to easily explain the presentinvention, and are not necessarily limited to those having all theconfigurations described. In addition, a portion of a configuration ofan embodiment can be replaced with a configuration of anotherembodiment, and a configuration of another embodiment can be added to aconfiguration of an embodiment.

What is claimed is:
 1. A pattern inspection system which inspects animage of an inspection target pattern of an electronic device using anidentifier constituted by machine learning, based on the image of theinspection target pattern of the electronic device and data used tomanufacture the inspection target pattern, the system comprising: a dataselection unit which selects a photographing coordinate of a learningpattern used in the machine learning, based on information aboutphotographing the electronic device and pattern data used to manufacturea pattern of the electronic device.
 2. The pattern inspection systemaccording to claim 1, wherein the data selection unit analyzes a shapeof a pattern using the pattern data and/or analyzes a position of apattern on a semiconductor device.
 3. The pattern inspection systemaccording to claim 2, wherein the data selection unit detects a positionon the pattern data in which the shape of the same pattern exists, usinga plurality of patterns having the same shape detected by analyzing ashape of the pattern and the information about photographing theelectronic device, and executes statistical processing using at leastone of the detected number, a coordinate position on a semiconductorchip, a coordinate position on a wafer, and distance information betweenthe coordinate position on the semiconductor chip and the coordinateposition on the wafer, and the detected position.
 4. The patterninspection system according to claim 3, wherein the data selection unitexecutes statistical processing of the number of pixels of a verticaledge and a horizontal edge based on edge information of a patternobtained pattern data corresponding to the pattern image obtained byphotographing the electronic device to derive the same shape.
 5. Thepattern inspection system according to claim 1, wherein the dataselection unit divides the pattern data into small areas correspondingto an imaging field of view of the information about photographing theelectronic device, analyzes a shape of a pattern for each divided area,and analyzes a position of the pattern on the electronic device based ona pattern having the same shape as the shape of the analyzed pattern tospecify the photographing position.
 6. The pattern inspection systemaccording to claim 1, further comprising: a recipe creation unit whichgenerates a recipe of a photographing unit based on the coordinateselected by the data selection unit.
 7. The pattern inspection systemaccording to claim 6, further comprising: an identifier generation unitwhich executes machine learning based on the learning pattern imagephotographed by the recipe and a true value data corresponding to thelearning pattern image so as to generate the identifier.
 8. The patterninspection system according to claim 7, further comprising: a regionextraction unit which extracts one of a contour shape of at least apattern, a pattern region, and a non-pattern region from the image ofthe inspection target pattern by the identifier.
 9. The patterninspection system according to claim 7, wherein the identifier extractsa defect based on the image of the inspection target pattern.
 10. Thepattern inspection system according to claim 7, wherein identificationdata obtained by the identifier and the pattern data used to manufacturethe pattern of the electronic device are compared with each other so asto execute inspection.
 11. A pattern inspection method for inspecting animage of an inspection target pattern of an electronic device using anidentifier constituted by machine learning, based on the image of theinspection target pattern of the electronic device and data used tomanufacture the inspection target pattern, the method comprising:selecting a photographing coordinate of a learning pattern used in themachine learning, based on information about photographing theelectronic device and pattern data used to manufacture a pattern of theelectronic device.